Spatiotemporal trends of illegal activities from ranger-collected data in a Ugandan national park

• DOI: 10.1111/cobi.12538
• Publication Date: 2015-05-20T21:40:20Z
• Authors: R. Critchlow; A.J. Plumptre; M. Driciru; A. Rwetsiba; E.J. Stokes; C. Tumwesigye; F. Wanyama; C.M. Beale
• Keywords: Mammals; 0106 biological sciences; Conservation of Natural Resources; Meat; 2303; Parks, Recreational; 1105; Commerce; Agriculture; Bayes Theorem; Forestry; Models, Theoretical; 15. Life on land; 01 natural sciences; Animals; Uganda; 2309; Animal Husbandry

Within protected areas, biodiversity loss is often a consequence of illegal resource use. Understanding the patterns and extent of illegal activities is therefore essential for effective law enforcement and prevention of biodiversity declines. We used extensive data, commonly collected by ranger patrols in many protected areas, and Bayesian hierarchical models to identify drivers, trends, and distribution of multiple illegal activities within the Queen Elizabeth Conservation Area (QECA), Uganda. Encroachment (e.g., by pastoralists with cattle) and poaching of noncommercial animals (e.g., snaring bushmeat) were the most prevalent illegal activities within the QECA. Illegal activities occurred in different areas of the QECA. Poaching of noncommercial animals was most widely distributed within the national park. Overall, ecological covariates, although significant, were not useful predictors for occurrence of illegal activities. Instead, the location of illegal activities in previous years was more important. There were significant increases in encroachment and noncommercial plant harvesting (nontimber products) during the study period (1999-2012). We also found significant spatiotemporal variation in the occurrence of all activities. Our results show the need to explicitly model ranger patrol effort to reduce biases from existing uncorrected or capture per unit effort analyses. Prioritization of ranger patrol strategies is needed to target illegal activities; these strategies are determined by protected area managers, and therefore changes at a site-level can be implemented quickly. These strategies should also be informed by the location of past occurrences of illegal activity: the most useful predictor of future events. However, because spatial and temporal changes in illegal activities occurred, regular patrols throughout the protected area, even in areas of low occurrence, are also required.